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Abstract #3573

DeepDECOMPOSE: A Deep Learning based framework for solving DECOMPOSE QSM

Jingjia Chen1, Alfredo De Goyeneche1, and Chunlei Liu1,2
1Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States, 2Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States

Synopsis

Keywords: Quantitative Imaging, Quantitative Susceptibility mappingWe propose a deep learning (DL) approach to accelerate and improve the accuracy of the parameter fitting problem of DECOMPOSE-QSM. The approach allows a triple complex exponential model to be fitted in <1s on CPUs and <20ms on a GPU for a 256x256 image, vs. 5+ min for the original solver. The DL solver can be implemented with either fixed echo times or adaptive to a range of echo times and number of echoes. Trained with various additive noise levels, the DL-solver performs more robustly compared to the conventional optimization-based solver when the signal has a very low SNR.

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